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\title{Stage One Review Document\\Multimodal Music Mood Classification Approach for Music Recommendations} 
\author{\textbf{Asma Rafiq}\\asma.rafiq@eecs.qmul.ac.uk\\School of Electronic Engineering\\and Computer Science\\Queen Mary, University of London}
\date{\today}

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Digital music is a rapidly growing research area. The use of internet to access music web services for music-related information and content is common. Social/collaborative and automatic multimedia annotation/tagging is gaining popularity supported by crowd wisdom such as the tags used to describe a Youtube video. It provides opportunities for multimedia indexing, search, retrieval and content analysis. Such semantically annotated content can provide us with information like sentiments which are important part of human perception. We are particularly interested in tagging for music recommendations based on mood as an alternative to genre based classification. However, as the research expands, there is also a possibility to explore multimodal feature selection space where a user can navigate music space with genre or mood tags. In the past, researchers have explored the music mood classification using lyrics and audio content \cite{Laurier08}.\\ Association of mood to music can be subjective, therefore, we seek to classify music moods based on consensus from both experts and non-experts i.e. tag associated to music by experts such as in production music and mapping those tags to commercial music and then comparing the system by extracting mood tags associated with tracks from Last.fm by non-experts. The former mapping i.e. from production to commercial music will be based on content-based descriptor. The term content-based descriptor mainly refers here to quantifiable characteristics of the audio content of a piece of music. This will include low-level descriptor namely, timbre and time segmentation, as well as higher-level descriptors like rhythm, form, and melody. The next step will be to associate similar sounding songs of production music to commercial music with similar contextual descriptor. Contextual descriptors refers to the listeners’ contextual interpretation of an acoustic signal, that is, the words that people use to qualitatively describe musical sound, collected from online reviews, discussion, and tagging of music. Sources for contextual descriptors include these sites: last.fm, allmusic.com, wikipedia.org, youtube.com and musicbrainz.org.  The results of this research will be built into new visualization and recommendation tools, incorporated into the ILM Desktop Music Library system, to give recommendations to producers from both commercial music and production music. The producers can give feedback and hence, evaluate the success of the recommender system.\\
Another solution is to use ontology to reduce mood classification problem to reasoning and querying over ontologies by developing a mood ontology for this purpose to deal with local/closed world to provide efficient querying in ontologies in relation with the Music Ontology to utilise the network effect between the vocabularies of mood and music. Ontologies are effectively used to link terms and partial mapping. The solutions proposed shall form the foundation for the framework of a better Music Recommendation Systems combined with semantic web. 
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%The applications and devices to distribute music are improving continuously. The software to support music is increasingly utilizing semantic web and social networking services. But as the availability of the music on the web and music playing devices increase so does the complexity for the user to select and play songs of his/her choice. Users own many digital music files that they might have only listened to once, or not at all. It seems reasonable to suppose that with efficient ways to create a personalised order of users’ collections, as well as ways to explore hidden ``treasures” inside them, the value of their music collections would drastically increase. The win-win situation would be when the music player is familiar with user's music taste and/or the intentions of the user. Sophisticated Music Recommendation Systems are becoming increasingly popular, and people are using these systems to obtain and share music and its related information. The application of social media and semantic web to the collection, storage and review of personal information presents opportunities for improved and personalized music recommender systems. We present a review of the literature on the intention aware systems, recommender systems, and semantic web. These shall form the foundation for the framework of a better Music Recommendation Systems combined with social media and semantic web. This framework shall then be used to develop a prototype for the system design.
%Two main traditional application types address these general demands for personalization: Hypermedia Adaptive Systems and  Recommender Systems.  The Hypermedia Adaptive Systems are focused on exploring a certain hypermedia structure in order to help users to find the best way for their interests, while the Recommender Systems are focused on a network of Web resources, bound by explicit or virtual relations, aiming to provide users with customized structures of these resources.  


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	\= ~~~~~~ 	\= ACM ~~~~ 	\= ~~~~~~~~ 	\= Association for Computing Machinery\\ 	
	\>  			\> AHS		\>			\> Adaptive Hypermedia System\\
	\>  			\> API 		\>			\> Application Programming Interface\\
	\>			\> C4DM 	\> 			\> Centre for Digital Music \\
	\>			\> CMMR 	\> 			\> Computer Music Modeling and Retrieval \\
	\>			\> CMJ		\>			\> Computer Music Journal\\		
	\>  			\> DMCA 	\>			\> Digital Millennium Copyright Act\\
	\>			\> DMRN	 	\>			\> Digital Music Research Network\\
	\>			\> EECS		\>			\> Electronic Engineering and Computer Science \\
	\>  			\> FBML 		\>			\> FaceBook Markup Language\\
	\>			\> GUI 		\>			\> Graphical User Interface \\
	\>  			\> HTML 	\>			\> HyperText Markup Language\\
	\>			\> Hz		\>			\> Hertz \\
	\>			\> JMRN		\>			\> Journal of New Music Research\\
	\>			\> JSON		\>			\> JavaScript Object Notation \\
	\>			\> ILM		\>			\> I Like Music\\
	\>			\> MB		\>			\> Mega Byte\\
	\>			\> MIR		\>			\> Music Information Retrieval\\
	\>			\> ms		\>			\> Millisecond \\	
	\>			\> MRS 		\>			\> Music Recommender System \\
	\>  			\> OSN 		\>			\> Online Social Network\\
	\>  			\> PhP 		\>			\> HyperText Preprocessor\\
	\> 			\> RecSys 	\>			\> International Conference on Recommender Systems\\
	\> 			\> RMSE 	\>			\> Root-Mean-Square Error\\
	\>			\> SDLC 		\>			\> System Development Lifecycle \\
	\>			\> WOMRAD	\>			\> Workshop On Music Recommendation and Discovery \\	
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